Based in the U.S., the company provides multi-model freight transportation and last-mile delivery, handling over a million shipments each year. It supports major industries like eCommerce, retail, and manufacturing.
To manage growing demand, they implemented a flow-based rule driven chatbot along with a support team handling multichannel support across email, chat, WhatsApp, and web. The chatbot worked for simple questions based on pre-defined scripts but struggled with more complex or unclear messages, making it hard to keep up with today’s fast-moving logistics needs.
Even after rolling out basic automation, the company struggled with five costly support bottlenecks :
The company sought to reimagine its support infrastructure by integrating AI-powered automation to :
The company first adopted flow-based rule-driven chatbots several years ago as to automate shipment tracking and confirming order status. Initially, this reduced ticket volume and brought small wins. However, with customer expectations evolved and inquiries became more nuanced, these chatbots proved rigid and outdated.
To improve CX & operational efficiency, the leadership connected with us to explore a strategic AI upgrade. After several internal workshops, competitor benchmarking, and pilot discussions, a complete transition was envisioned for next-gen AI assistant and smart AI agent routing system.
The implementation was executed in five structured phases over a 6-month window.
The newly implemented AI assistant and AI agents transformed the support process with capabilities tailor-made for the logistics industry :
Pulls real-time data from CRM, ERP, and Helpdesk to enrich responses.
Suggests replies, pre-fills forms, and routes complex queries with full context.
+ Supports human agents through collaboration, operational automation, reducing errors and boosting efficiency.
Through the implementation of AI assistants and smart AI agents, the logistics company experienced a dramatic operational and financial uplift.
$812K+ in Annual Support Savings, lowering total cost by over 40% with enhanced support quality.
58.4% Reduction in Average Handling Time, from 15–20 minutes to under 6 minutes per query.
52% Query Deflection, with over 500 daily queries now resolved autonomously.
Metric | Before AI System | After AI Assistant + Agents | Change (%) |
---|---|---|---|
Daily Query Volume | ~800–1,000 | 950 avg., 500+ auto-resolved | ↑ 18% throughput |
Average Handling Time | 15–20 min | < 6 min | ↓ 58% |
First-Contact Resolution | 62% | 88% | ↑ 26% |
Routing Accuracy | 65–70% manual | >92% AI-routed | ↑ 30% |
CSAT | 72% | 89% | ↑ 17% |
Query Understanding | Tree-based, low context | NLP-driven, multi-turn clarification | ↑ Context Accuracy |
Sentiment Awareness | None | Live sentiment & feedback detection | Proactive Handling |
Centralized dashboard with KPIs, sentiment tracking, and issue trends.
Context-aware escalations reduced agent dependency and resolution cycles.
Onboarding time halved with AI-curated historical insights and live reply suggestions.
We were overwhelmed by repetitive queries and rising support costs. AI helped us turn that around—automating responses, easing agent load, and delivering faster, smarter service.
Company plans to build on this success with several forward-looking initiatives :
Integration of voice bots for phone-based support powered by the same NLP engine.
Proactive customer outreach - notifying customers about potential delays, alternate delivery slots, & personalized alerts.
Using AI insights to refine CX strategy, optimize delivery workflows, and support demand forecasting.
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